- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Variable selection in Poisson regression model using invasive weed optimization algorithm | ||
| IRAQI JOURNAL OF STATISTICAL SCIENCES | ||
| Article 3, Volume 16, Issue 3, 2019, Pages 39-54 PDF (682.65 K) | ||
| DOI: 10.33899/iqjoss.2019.0164173 | ||
| Authors | ||
| Ghada yosif ismail* 1; Zakariya Yahya Algamal* 2 | ||
| 1Department of Mechanical Techniques | ||
| 2Dept. of Statistics and Informatics/ college of computer sciences and mathematics/ University of Mosul, Mosul, iraq | ||
| Abstract | ||
| Variable selection is a very helpful procedure for improving prediction accuracy by finding the most important variables that are related to the response variable. Poisson regression model has received much attention in several science fields for modeling count data. Invasive weed optimization algorithm (IWO) is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, IWO algorithm is proposed to perform variable selection for Poisson regression model. Extensive simulation studies and real data application are conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. The results proved the efficiency of our proposed methods and it outperforms other popular methods. | ||
|
Statistics Article View: 490 PDF Download: 1,467 |
||